






"""simple docstring"""





from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
__lowerCamelCase							=  2_00
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be smaller than N_POPULATION.
__lowerCamelCase							=  50
# Probability that an element of a generation can mutate, changing one of its genes.
# This will guarantee that all genes will be used during evolution.
__lowerCamelCase							=  0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 10_00))



def   a    (     __UpperCAmelCase      :    str      ,   __UpperCAmelCase      :    str  )    ->							tuple[str, float]:
						__magic_name__:	Any							=       len([g for position, g in enumerate(__UpperCAmelCase  ) if g == main_target[position]]  )
						return (item, float(__UpperCAmelCase  ))



def   a    (     __UpperCAmelCase      :    str      ,   __UpperCAmelCase      :    str  )    ->							tuple[str, str]:
						__magic_name__:	Tuple							=       random.randint(0      ,   len(__UpperCAmelCase  ) - 1  )
						__magic_name__:	Optional[Any]							=       parent_a[:random_slice] + parent_a[random_slice:]
						__magic_name__:	Optional[Any]							=       parent_a[:random_slice] + parent_a[random_slice:]
						return (child_a, child_a)



def   a    (     __UpperCAmelCase      :    str      ,   __UpperCAmelCase      :    list[str]  )    ->							str:
						__magic_name__:	Union[str, Any]							=       list(__UpperCAmelCase  )
						if random.uniform(0      ,   1  ) < MUTATION_PROBABILITY:
												__magic_name__:	Any							=       random.choice(__UpperCAmelCase  )
						return "".join(__UpperCAmelCase  )



def   a    (     __UpperCAmelCase      :    tuple[str, float]      ,   __UpperCAmelCase      :    list[tuple[str, float]]      ,   __UpperCAmelCase      :    list[str]      ,   )    ->							list[str]:
						__magic_name__:	Union[str, Any]							=       []
						# Generate more children proportionally to the fitness score.
						__magic_name__:	List[str]							=       int(parent_a[1] * 1_0_0  ) + 1
						__magic_name__:	Union[str, Any]							=       1_0 if child_n >= 1_0 else child_n
						for _ in range(__UpperCAmelCase  ):
												__magic_name__:	Union[str, Any]							=       population_score[random.randint(0      ,   __UpperCAmelCase  )][0]

												__magic_name__,			__magic_name__:	List[str]							=       crossover(parent_a[0]      ,   __UpperCAmelCase  )
												# Append new string to the population list.
												pop.append(mutate(__UpperCAmelCase      ,   __UpperCAmelCase  )  )
												pop.append(mutate(__UpperCAmelCase      ,   __UpperCAmelCase  )  )
						return pop



def   a    (     __UpperCAmelCase      :    str      ,   __UpperCAmelCase      :    list[str]      ,   __UpperCAmelCase      :    bool = True  )    ->							tuple[int, int, str]:

						# Verify if N_POPULATION is bigger than N_SELECTED
						if N_POPULATION < N_SELECTED:
												__magic_name__:	Any							=       f'{N_POPULATION} must be bigger than {N_SELECTED}'
												raise ValueError(__UpperCAmelCase  )
						# Verify that the target contains no genes besides the ones inside genes variable.
						__magic_name__:	Dict							=       sorted({c for c in target if c not in genes}  )
						if not_in_genes_list:
												__magic_name__:	List[str]							=       f'{not_in_genes_list} is not in genes list, evolution cannot converge'
												raise ValueError(__UpperCAmelCase  )

						# Generate random starting population.
						__magic_name__:	Optional[Any]							=       []
						for _ in range(__UpperCAmelCase  ):
												population.append("""""".join([random.choice(__UpperCAmelCase  ) for i in range(len(__UpperCAmelCase  )  )]  )  )

						# Just some logs to know what the algorithms is doing.
						__magic_name__,			__magic_name__:	Optional[int]							=       0, 0

						# This loop will end when we find a perfect match for our target.
						while True:
												generation += 1
												total_population += len(__UpperCAmelCase  )

												# Random population created. Now it's time to evaluate.

												# Adding a bit of concurrency can make everything faster,
												#
												# import concurrent.futures
												# population_score: list[tuple[str, float]] = []
												# with concurrent.futures.ThreadPoolExecutor(
												#                                   max_workers=NUM_WORKERS) as executor:
												#     futures = {executor.submit(evaluate, item) for item in population}
												#     concurrent.futures.wait(futures)
												#     population_score = [item.result() for item in futures]
												#
												# but with a simple algorithm like this, it will probably be slower.
												# We just need to call evaluate for every item inside the population.
												__magic_name__:	Tuple							=       [evaluate(__UpperCAmelCase      ,   __UpperCAmelCase  ) for item in population]

												# Check if there is a matching evolution.
												__magic_name__:	List[Any]							=       sorted(__UpperCAmelCase      ,   key=lambda __UpperCAmelCase  : x[1]      ,   reverse=__UpperCAmelCase  )
												if population_score[0][0] == target:
																		return (generation, total_population, population_score[0][0])

												# Print the best result every 10 generation.
												# Just to know that the algorithm is working.
												if debug and generation % 1_0 == 0:
																		print(
																		    f'\nGeneration: {generation}'
																		    f'\nTotal Population:{total_population}'
																		    f'\nBest score: {population_score[0][1]}'
																		    f'\nBest string: {population_score[0][0]}'  )

												# Flush the old population, keeping some of the best evolutions.
												# Keeping this avoid regression of evolution.
												__magic_name__:	int							=       population[: int(N_POPULATION / 3  )]
												population.clear()
												population.extend(__UpperCAmelCase  )
												# Normalize population score to be between 0 and 1.
												__magic_name__:	Dict							=       [
												    (item, score / len(__UpperCAmelCase  )) for item, score in population_score
												]

												# This is selection
												for i in range(__UpperCAmelCase  ):
																		population.extend(select(population_score[int(__UpperCAmelCase  )]      ,   __UpperCAmelCase      ,   __UpperCAmelCase  )  )
																		# Check if the population has already reached the maximum value and if so,
																		# break the cycle.  If this check is disabled, the algorithm will take
																		# forever to compute large strings, but will also calculate small strings in
																		# a far fewer generations.
																		if len(__UpperCAmelCase  ) > N_POPULATION:
																								break


if __name__ == "__main__":
							__lowerCamelCase							=  (
							    'This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!'
							)
							__lowerCamelCase							=  list(
							    ' ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm'
							    'nopqrstuvwxyz.,;!?+-*#@^\'èéòà€ù=)(&%$£/\\'
							)
							__lowerCamelCase		,					__lowerCamelCase		,					__lowerCamelCase							=  basic(target_str, genes_list)
							print(
							    f'''\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}'''
							)





import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    Pipeline,
    ZeroShotClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
__lowerCAmelCase     :	Optional[Any]       			=  {'LayoutLMv2Config', 'LayoutLMv3Config'}




@is_pipeline_test
class 			UpperCAmelCase_     (					unittest.TestCase	):


  '''simple docstring'''







  a__       			=						MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
  a__       			=						TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

  if model_mapping is not None:
    a__       			=						{config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
  if tf_model_mapping is not None:
    a__       			=						{
        config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
    }
  def 				_lowercase				(					self     :					Dict     ,				UpperCamelCase__     :					Tuple     ,				UpperCamelCase__     :					Optional[int]     ,				UpperCamelCase__     :					List[str]       )     ->      List[str]:







    """simple docstring"""






    __magic_name__								=			ZeroShotClassificationPipeline(
        model=UpperCamelCase__     ,				tokenizer=UpperCamelCase__     ,				candidate_labels=["""polics""", """health"""]       )
    return classifier, ["Who are you voting for in 2020?", "My stomach hurts."]
  def 				_lowercase				(					self     :					int     ,				UpperCamelCase__     :					Any     ,				UpperCamelCase__     :					List[Any]       )     ->      str:







    """simple docstring"""






    __magic_name__								=			classifier("""Who are you voting for in 2020?"""     ,				candidate_labels="""politics"""       )
    self.assertEqual(UpperCamelCase__     ,				{"""sequence""": ANY(UpperCamelCase__       ), """labels""": [ANY(UpperCamelCase__       )], """scores""": [ANY(UpperCamelCase__       )]}       )

    # No kwarg
    __magic_name__								=			classifier("""Who are you voting for in 2020?"""     ,				["""politics"""]       )
    self.assertEqual(UpperCamelCase__     ,				{"""sequence""": ANY(UpperCamelCase__       ), """labels""": [ANY(UpperCamelCase__       )], """scores""": [ANY(UpperCamelCase__       )]}       )

    __magic_name__								=			classifier("""Who are you voting for in 2020?"""     ,				candidate_labels=["""politics"""]       )
    self.assertEqual(UpperCamelCase__     ,				{"""sequence""": ANY(UpperCamelCase__       ), """labels""": [ANY(UpperCamelCase__       )], """scores""": [ANY(UpperCamelCase__       )]}       )

    __magic_name__								=			classifier("""Who are you voting for in 2020?"""     ,				candidate_labels="""politics, public health"""       )
    self.assertEqual(
        UpperCamelCase__     ,				{"""sequence""": ANY(UpperCamelCase__       ), """labels""": [ANY(UpperCamelCase__       ), ANY(UpperCamelCase__       )], """scores""": [ANY(UpperCamelCase__       ), ANY(UpperCamelCase__       )]}       )
    self.assertAlmostEqual(sum(nested_simplify(outputs["""scores"""]       )       )     ,				1.0       )

    __magic_name__								=			classifier("""Who are you voting for in 2020?"""     ,				candidate_labels=["""politics""", """public health"""]       )
    self.assertEqual(
        UpperCamelCase__     ,				{"""sequence""": ANY(UpperCamelCase__       ), """labels""": [ANY(UpperCamelCase__       ), ANY(UpperCamelCase__       )], """scores""": [ANY(UpperCamelCase__       ), ANY(UpperCamelCase__       )]}       )
    self.assertAlmostEqual(sum(nested_simplify(outputs["""scores"""]       )       )     ,				1.0       )

    __magic_name__								=			classifier(
        """Who are you voting for in 2020?"""     ,				candidate_labels="""politics"""     ,				hypothesis_template="""This text is about {}"""       )
    self.assertEqual(UpperCamelCase__     ,				{"""sequence""": ANY(UpperCamelCase__       ), """labels""": [ANY(UpperCamelCase__       )], """scores""": [ANY(UpperCamelCase__       )]}       )

    # https://github.com/huggingface/transformers/issues/13846
    __magic_name__								=			classifier(["""I am happy"""]     ,				["""positive""", """negative"""]       )
    self.assertEqual(
        UpperCamelCase__     ,				[
            {"""sequence""": ANY(UpperCamelCase__       ), """labels""": [ANY(UpperCamelCase__       ), ANY(UpperCamelCase__       )], """scores""": [ANY(UpperCamelCase__       ), ANY(UpperCamelCase__       )]}
            for i in range(1       )
        ]     ,				)
    __magic_name__								=			classifier(["""I am happy""", """I am sad"""]     ,				["""positive""", """negative"""]       )
    self.assertEqual(
        UpperCamelCase__     ,				[
            {"""sequence""": ANY(UpperCamelCase__       ), """labels""": [ANY(UpperCamelCase__       ), ANY(UpperCamelCase__       )], """scores""": [ANY(UpperCamelCase__       ), ANY(UpperCamelCase__       )]}
            for i in range(2       )
        ]     ,				)

    with self.assertRaises(UpperCamelCase__       ):
      classifier(""""""     ,				candidate_labels="""politics"""       )

    with self.assertRaises(UpperCamelCase__       ):
      classifier(UpperCamelCase__     ,				candidate_labels="""politics"""       )

    with self.assertRaises(UpperCamelCase__       ):
      classifier("""Who are you voting for in 2020?"""     ,				candidate_labels=""""""       )

    with self.assertRaises(UpperCamelCase__       ):
      classifier("""Who are you voting for in 2020?"""     ,				candidate_labels=UpperCamelCase__       )

    with self.assertRaises(UpperCamelCase__       ):
      classifier(
          """Who are you voting for in 2020?"""     ,				candidate_labels="""politics"""     ,				hypothesis_template="""Not formatting template"""     ,				)

    with self.assertRaises(UpperCamelCase__       ):
      classifier(
          """Who are you voting for in 2020?"""     ,				candidate_labels="""politics"""     ,				hypothesis_template=UpperCamelCase__     ,				)

    self.run_entailment_id(UpperCamelCase__       )
  def 				_lowercase				(					self     :					Dict     ,				UpperCamelCase__     :					Pipeline       )     ->      Dict:







    """simple docstring"""






    __magic_name__								=			zero_shot_classifier.model.config
    __magic_name__								=			config.labelaid
    __magic_name__								=			zero_shot_classifier.entailment_id

    __magic_name__								=			{"""LABEL_0""": 0, """LABEL_1""": 1, """LABEL_2""": 2}
    self.assertEqual(zero_shot_classifier.entailment_id     ,				-1       )

    __magic_name__								=			{"""entailment""": 0, """neutral""": 1, """contradiction""": 2}
    self.assertEqual(zero_shot_classifier.entailment_id     ,				0       )

    __magic_name__								=			{"""ENTAIL""": 0, """NON-ENTAIL""": 1}
    self.assertEqual(zero_shot_classifier.entailment_id     ,				0       )

    __magic_name__								=			{"""ENTAIL""": 2, """NEUTRAL""": 1, """CONTR""": 0}
    self.assertEqual(zero_shot_classifier.entailment_id     ,				2       )

    __magic_name__								=			original_labelaid
    self.assertEqual(UpperCamelCase__     ,				zero_shot_classifier.entailment_id       )
  @require_torch
  def 				_lowercase				(					self     :					Optional[int]       )     ->      Union[str, Any]:







    """simple docstring"""






    __magic_name__								=			pipeline(
        """zero-shot-classification"""     ,				model="""sshleifer/tiny-distilbert-base-cased-distilled-squad"""     ,				framework="""pt"""     ,				)
    # There was a regression in 4.10 for this
    # Adding a test so we don't make the mistake again.
    # https://github.com/huggingface/transformers/issues/13381#issuecomment-912343499
    zero_shot_classifier(
        """Who are you voting for in 2020?""" * 100     ,				candidate_labels=["""politics""", """public health""", """science"""]       )
  @require_torch
  def 				_lowercase				(					self     :					Optional[int]       )     ->      Tuple:







    """simple docstring"""






    __magic_name__								=			pipeline(
        """zero-shot-classification"""     ,				model="""sshleifer/tiny-distilbert-base-cased-distilled-squad"""     ,				framework="""pt"""     ,				)
    __magic_name__								=			zero_shot_classifier(
        """Who are you voting for in 2020?"""     ,				candidate_labels=["""politics""", """public health""", """science"""]       )

    self.assertEqual(
        nested_simplify(UpperCamelCase__       )     ,				{
            """sequence""": """Who are you voting for in 2020?""",
            """labels""": ["""science""", """public health""", """politics"""],
            """scores""": [0.333, 0.333, 0.333],
        }     ,				)
  @require_tf
  def 				_lowercase				(					self     :					Optional[Any]       )     ->      List[str]:







    """simple docstring"""






    __magic_name__								=			pipeline(
        """zero-shot-classification"""     ,				model="""sshleifer/tiny-distilbert-base-cased-distilled-squad"""     ,				framework="""tf"""     ,				)
    __magic_name__								=			zero_shot_classifier(
        """Who are you voting for in 2020?"""     ,				candidate_labels=["""politics""", """public health""", """science"""]       )

    self.assertEqual(
        nested_simplify(UpperCamelCase__       )     ,				{
            """sequence""": """Who are you voting for in 2020?""",
            """labels""": ["""science""", """public health""", """politics"""],
            """scores""": [0.333, 0.333, 0.333],
        }     ,				)
  @slow
  @require_torch
  def 				_lowercase				(					self     :					Any       )     ->      List[Any]:







    """simple docstring"""






    __magic_name__								=			pipeline("""zero-shot-classification"""     ,				model="""roberta-large-mnli"""     ,				framework="""pt"""       )
    __magic_name__								=			zero_shot_classifier(
        """Who are you voting for in 2020?"""     ,				candidate_labels=["""politics""", """public health""", """science"""]       )

    self.assertEqual(
        nested_simplify(UpperCamelCase__       )     ,				{
            """sequence""": """Who are you voting for in 2020?""",
            """labels""": ["""politics""", """public health""", """science"""],
            """scores""": [0.976, 0.015, 0.009],
        }     ,				)
    __magic_name__								=			zero_shot_classifier(
        """The dominant sequence transduction models are based on complex recurrent or convolutional neural networks"""
        """ in an encoder-decoder configuration. The best performing models also connect the encoder and decoder"""
        """ through an attention mechanism. We propose a new simple network architecture, the Transformer, based"""
        """ solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two"""
        """ machine translation tasks show these models to be superior in quality while being more parallelizable"""
        """ and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014"""
        """ English-to-German translation task, improving over the existing best results, including ensembles by"""
        """ over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new"""
        """ single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small"""
        """ fraction of the training costs of the best models from the literature. We show that the Transformer"""
        """ generalizes well to other tasks by applying it successfully to English constituency parsing both with"""
        """ large and limited training data."""     ,				candidate_labels=["""machine learning""", """statistics""", """translation""", """vision"""]     ,				multi_label=UpperCamelCase__     ,				)
    self.assertEqual(
        nested_simplify(UpperCamelCase__       )     ,				{
            """sequence""": (
                """The dominant sequence transduction models are based on complex recurrent or convolutional neural"""
                """ networks in an encoder-decoder configuration. The best performing models also connect the"""
                """ encoder and decoder through an attention mechanism. We propose a new simple network"""
                """ architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence"""
                """ and convolutions entirely. Experiments on two machine translation tasks show these models to be"""
                """ superior in quality while being more parallelizable and requiring significantly less time to"""
                """ train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,"""
                """ improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014"""
                """ English-to-French translation task, our model establishes a new single-model state-of-the-art"""
                """ BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training"""
                """ costs of the best models from the literature. We show that the Transformer generalizes well to"""
                """ other tasks by applying it successfully to English constituency parsing both with large and"""
                """ limited training data."""
            ),
            """labels""": ["""translation""", """machine learning""", """vision""", """statistics"""],
            """scores""": [0.817, 0.713, 0.018, 0.018],
        }     ,				)





  @slow
  @require_tf
  def 				_lowercase				(					self     :					str       )     ->      Optional[Any]:







    """simple docstring"""






    __magic_name__								=			pipeline("""zero-shot-classification"""     ,				model="""roberta-large-mnli"""     ,				framework="""tf"""       )
    __magic_name__								=			zero_shot_classifier(
        """Who are you voting for in 2020?"""     ,				candidate_labels=["""politics""", """public health""", """science"""]       )

    self.assertEqual(
        nested_simplify(UpperCamelCase__       )     ,				{
            """sequence""": """Who are you voting for in 2020?""",
            """labels""": ["""politics""", """public health""", """science"""],
            """scores""": [0.976, 0.015, 0.009],
        }     ,				)
    __magic_name__								=			zero_shot_classifier(
        """The dominant sequence transduction models are based on complex recurrent or convolutional neural networks"""
        """ in an encoder-decoder configuration. The best performing models also connect the encoder and decoder"""
        """ through an attention mechanism. We propose a new simple network architecture, the Transformer, based"""
        """ solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two"""
        """ machine translation tasks show these models to be superior in quality while being more parallelizable"""
        """ and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014"""
        """ English-to-German translation task, improving over the existing best results, including ensembles by"""
        """ over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new"""
        """ single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small"""
        """ fraction of the training costs of the best models from the literature. We show that the Transformer"""
        """ generalizes well to other tasks by applying it successfully to English constituency parsing both with"""
        """ large and limited training data."""     ,				candidate_labels=["""machine learning""", """statistics""", """translation""", """vision"""]     ,				multi_label=UpperCamelCase__     ,				)
    self.assertEqual(
        nested_simplify(UpperCamelCase__       )     ,				{
            """sequence""": (
                """The dominant sequence transduction models are based on complex recurrent or convolutional neural"""
                """ networks in an encoder-decoder configuration. The best performing models also connect the"""
                """ encoder and decoder through an attention mechanism. We propose a new simple network"""
                """ architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence"""
                """ and convolutions entirely. Experiments on two machine translation tasks show these models to be"""
                """ superior in quality while being more parallelizable and requiring significantly less time to"""
                """ train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,"""
                """ improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014"""
                """ English-to-French translation task, our model establishes a new single-model state-of-the-art"""
                """ BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training"""
                """ costs of the best models from the literature. We show that the Transformer generalizes well to"""
                """ other tasks by applying it successfully to English constituency parsing both with large and"""
                """ limited training data."""
            ),
            """labels""": ["""translation""", """machine learning""", """vision""", """statistics"""],
            """scores""": [0.817, 0.713, 0.018, 0.018],
        }     ,				)

